#!/bin/bash


#集合通信参数,不需要修改
export RANK_SIZE=8


# 数据集路径,保持为空,不需要修改
data_path=""

#网络名称,同目录名称,需要模型审视修改
Network="RCF"

#训练batch_size,,需要模型审视修改
batch_size=3

#参数校验，不需要修改
# 参数校验，data_path为必传参数，其他参数的增删由模型自身决定；此处新增参数需在上面有定义并赋值
for para in $*
do
    if [[ $para == --data_path* ]];then
        data_path=`echo ${para#*=}`
    fi
done

#校验是否传入data_path,不需要修改
if [[ $data_path == "" ]];then
    echo "[Error] para \"data_path\" must be confing"
    exit 1
fi

###############指定训练脚本执行路径###############
# cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
cur_path=`pwd`
cur_path_last_dirname=${cur_path##*/}
if [ x"${cur_path_last_dirname}" == x"test" ];then
    test_path_dir=${cur_path}
    cd ..
    cur_path=`pwd`
else
    test_path_dir=${cur_path}/test
fi


#################启动训练脚本#################
#训练开始时间，不需要修改
start_time=$(date +%s)
# 非平台场景时source 环境变量
check_etp_flag=`env | grep etp_running_flag`
etp_flag=`echo ${check_etp_flag#*=}`
if [ x"${etp_flag}" != x"true" ];then
    source ${test_path_dir}/env_npu.sh
fi  

# 数据集软链到脚本工程内部,并生成so文件
cur_path=`pwd`
mkdir -p ${cur_path}/cxx/lib
if [ ! -f "${cur_path}/cxx/lib/solve_csa.so" ];then
    cd ${cur_path}/cxx/src
    source build.sh
    cd ${cur_path}
fi
mkdir -p ${cur_path}/data
default_data_path=${cur_path}/data
ln -s ${data_path} ${default_data_path}/.

#执行训练脚本，以下传参不需要修改，其他需要模型审视修改
export ASCEND_SLOG_PRINT_TO_STDOUT=0
export ASCEND_GLOBAL_LOG_LEVEL=3
export TASK_QUEUE_ENABLE=1
export PTCOPY_ENABLE=1
export COMBINED_ENABLE=1
export SWITCH_MM_OUTPUT_ENABLE=1

KERNEL_NUM=$(($(nproc)/8))
for i in $(seq 0 7)
do
ASCEND_DEVICE_ID=$i

#创建DeviceID输出目录，不需要修改
if [ -d ${test_path_dir}/output/${ASCEND_DEVICE_ID} ];then
    rm -rf ${test_path_dir}/output/$ASCEND_DEVICE_ID
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
else
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
fi

if [ $(uname -m) = "aarch64" ]
then
    PID_START=$((KERNEL_NUM * i))
    PID_END=$((PID_START + KERNEL_NUM - 1))
    {
    taskset -c $PID_START-$PID_END python3 train8p.py --npu=${i}
    if [ "$ASCEND_DEVICE_ID" -eq "0" ];then 
        if [ -d "${cur_path}/results/val" ];then
            cd ${cur_path}/results/val
            rm -rf all
            rm -rf mat
            rm -rf png
            rm -rf eval
            cd ${cur_path}
        else
            mkdir -p ${cur_path}/results/val
        fi
        python3 test.py --resume='ckpt/only-final-lr-0.008-iter-50000.pth'
        python3 main.py
    fi
    } > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
else
    {
    python3 train8p.py --npu=${i}
    if [ "$ASCEND_DEVICE_ID" -eq "0" ];then 
        if [ -d "${cur_path}/results/val" ];then
            cd ${cur_path}/results/val
            rm -rf all
            rm -rf mat
            rm -rf png
            rm -rf eval
            cd ${cur_path}
        else
            mkdir -p ${cur_path}/results/val
        fi
        python3 test.py --resume='ckpt/only-final-lr-0.008-iter-50000.pth'
        python3 main.py
    fi
    } > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
fi
done

#8p情况下仅0卡(主节点)有完整日志,因此后续日志提取仅涉及0卡
ASCEND_DEVICE_ID=0
    
wait

##################获取训练数据################
#训练结束时间，不需要修改
end_time=$(date +%s)
e2e_time=$(( $end_time - $start_time ))

#结果打印，不需要修改
echo "------------------ Final result ------------------"
#输出性能FPS，需要模型审视修改
FPS=`grep -a 'FPS'  ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk -F ":" '{print $NF}'|awk 'NR==1{max=$1;next}{max=max>$1?max:$1}END{print max}'`
#打印，不需要修改
echo "Final Performance images/sec : $FPS"

#输出训练精度,需要模型审视修改
train_accuracy=`grep -a 'ODS' ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk -F "," '{print $1}'|awk -F "=" '{print $NF}'`
#打印，不需要修改
echo "Final Train Accuracy : ${train_accuracy}"

#打印，不需要修改
echo "E2E Training Duration sec : $e2e_time"

#性能看护结果汇总
#训练用例信息，不需要修改
BatchSize=${batch_size}
DeviceType=`uname -m`
CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'

#获取性能数据，不需要修改
#吞吐量
ActualFPS=`awk -v x="$FPS" -v y="$RANK_SIZE" 'BEGIN{printf "%.3f\n", x}'`
#单迭代训练时长
TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'*1000/'${FPS}'}'`

#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要模型审视修改
grep -a 'avg_loss'  ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print $4}'|awk -F ":" '{print $2}' >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.log
#最后一个迭代loss值，不需要修改
ActualLoss=`awk 'END {print}' ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.log`

#关键信息打印到${CaseName}.log中，不需要修改
echo "Network = ${Network}" > ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "RankSize = ${RANK_SIZE}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "BatchSize = ${BatchSize}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "DeviceType = ${DeviceType}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CaseName = ${CaseName}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualFPS = ${ActualFPS}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainingTime = ${TrainingTime}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainAccuracy = ${train_accuracy}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualLoss = ${ActualLoss}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "E2ETrainingTime = ${e2e_time}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log